Cross-entropy Loss Function Research Articles

A Secure High-Order Gene Interaction Detection Algorithm Based on Deep Neural Network.

Identifying high-order Single Nucleotide Polymorphism (SNP) interactions of additive genetic model is crucial for detecting complex disease gene-type and predicting pathogenic genes of various disorders. We present a novel framework for high-order gene interactions detection, not directly identifying individual site, but based on Deep Learning (DL) method with Differential Privacy (DP), termed as Deep-DPGI. Firstly, integrate loss functions including cross-entropy and focal loss function to train the model parameters that minimize the value of loss. Secondly, use the layer-wise relevance analysis method to measure relevance difference between neurons weight and outputting results. Deep-DPGI disturbs neuron weight by adaptive noising mechanism, protecting the safety of high-order gene interactions and balancing the privacy and utility. Specifically, more noise is added to gradients of neurons that is less relevance with the outputs, less noise to gradients that more relevance. Finally, Experiments on simulated and real datasets demonstrate that Deep-DPGI not only improve the power of high-order gene interactions detection in with marginal and without marginal effect of complex disease models, but also prevent the disclosure of sensitive information effectively.

Evaluation Model of Athletes’ Lower Extremity Training Ability Based on LSTM Algorithm

To achieve intelligent evaluation of the lower limb movement ability of athletes with sports disabilities, this article selects young athletes and middle-aged and young athletes with sports disabilities as the research objects, and healthy young athletes as the control group. Gait videos, GRF and knee angles of the subjects were collected to extract and analyse gait contours and features. The improved Visual Background extractor (ViBe) algorithm has the highest accuracy of 0.978 in PETS2006 video sequences; the recall rate of three algorithms in Highway video sequence is the highest, and the recall rate of improved ViBe algorithm is the highest, up to 0.965. AtƐ=0.7, the accuracy of the improved myloss training set is higher than that corresponding to other values; when the number of iterations is 98, the accuracy rate of improved myloss training set is 0.963, while the accuracy rate corresponding to the cross entropy loss function is 0.945. When the number of iterations is 151, the accuracy rate of the Xeption LSTM model is 0.956, higher than that of other models. Among corresponding mean ± standard deviation (GSA-MS) indicators, the GSA-MS values of Group L are significantly higher than those of group N and group Z (P<0.001). The correlation between GAS indicators and Gait Abnormality Rating Scale (GARS-M) is strong, with a correlation coefficient of 0.90.

A colour vision test assisted by neural networks

Aims/Purpose: Traditional computerized colour vision tests resort to the magnitude of chromatic thresholds to determine the quality of the colour vision of an observer. The limits of perceived colours are tested against a background that will be conspicuous to normal colour vision observers, but inconspicuous to colour vision deficient (CVD) observers. The purpose of this work was to implement a Neural Network capable of performing independent and automatic classification of the colour vision of an observer, using as inputs the sRGB chromaticity coordinates, response times, and eye movements obtained using a colour vision test on a computer screen calibrated in colour and luminance (Display++).Methods: Chromatic discrimination limits were estimated using a staircase procedure that tested colour vision close to the dichromatic confusion lines. Only the last 8 responses were considered in the analysis. 50 normal colour vision observers and 8 CVD observers performed the test on Display++. CVD participants were diagnosed using the results of the anomaloscope and the CAD colour vision tests. A Sequential Neural Network was implemented with a dense structure (256–128–64‐16‐1 nodes), with relu and sigmoid activation functions, dropout commands to prevent overfitting, the Adam optimizer, and the binary crossentropy loss function. The performance of the neural network was estimated by computing its accuracy, where variables are related to the results obtained after training the Neural Network and inspecting the test results. The database was divided into a training/test following different splitting strategies: T1 observers' responses considering sRGB coordinates, response times, and eye movements. T2 observers' responses considering only sRGB coordinates.Results: The averages and standard deviations of the estimated accuracy for five consecutive trials with a random division between training/test for conditions T1 and T2 was 91% ± 7% and 93% ± 5% respectively. It was found that the implemented neural network had an accuracy of more than 90% across all conditions.Conclusions: The accuracies found across conditions considered seem to indicate that the implemented Neural Network can analyse and classify the colour vision of human observers, with an accuracy greater than 90% of success.

A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification.

Accurate classification techniques are essential for the early diagnosis and treatment of patients with diabetic retinopathy (DR). However, the limited amount of annotated DR data poses a challenge for existing deep-learning models. This article proposes a difficulty-aware and task-augmentation method based on meta-learning (DaTa-ML) model for few-shot DR classification with fundus images. The difficulty-aware (Da) method operates by dynamically modifying the cross-entropy loss function applied to learning tasks. This methodology has the ability to intelligently down-weight simpler tasks, while simultaneously prioritizing more challenging tasks. These adjustments occur automatically and aim to optimize the learning process. Additionally, the task-augmentation (Ta) method is used to enhance the meta-training process by augmenting the number of tasks through image rotation and improving the feature-extraction capability. To implement the expansion of the meta-training tasks, various task instances can be sampled during the meta-training stage. Ultimately, the proposed Ta method was introduced to optimize the initialization parameters and enhance the meta-generalization performance of the model. The DaTa-ML model showed promising results by effectively addressing the challenges associated with few-shot DR classification. The Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 blindness detection data set was used to evaluate the DaTa-ML model. The results showed that with only 1% of the training data (5-way, 20-shot) and a single update step (training time reduced by 90%), the DaTa-ML model had an accuracy rate of 89.6% on the test data, which is a 1.7% improvement over the transfer-learning method [i.e., residual neural network (ResNet)50 pre-trained on ImageNet], and a 16.8% improvement over scratch-built models (i.e., ResNet50 without pre-trained weights), despite having fewer trainable parameters (the parameters used by the DaTa-ML model are only 0.47% of the ResNet50 parameters). The DaTa-ML model provides a more efficient DR classification solution with little annotated data and has significant advantages over state-of-the-art methods. Thus, it could be used to guide and assist ophthalmologists to determine the severity of DR.

CCN+: A neuro-symbolic framework for deep learning with requirements

For their outstanding ability of finding hidden patterns in data, deep learning models have been extensively applied in many different domains. However, recent works have shown that, if a set of requirements expressing inherent knowledge about the problem at hand is given, then neural networks often fail to comply with them. This represents a major drawback for deep learning models, as requirements compliance is normally considered a necessary condition for standard software deployment. In this paper, we propose a novel neuro-symbolic framework able to make any neural network compliant by design to a given set of requirements over the output space expressed in full propositional logic. This framework, called CCN+, integrates the requirements into the output layer of the neural network by applying multiple inference rules that ensure compliance with the requirements and adapts the standard binary cross-entropy loss function to the requirement output layer. As a result, not only the outputted predictions are guaranteed to be compliant with the requirements, but the neural network itself learns how to exploit the domain knowledge expressed by the requirements to get better performance. We conduct an extensive experimental evaluation of CCN+ on 19 real-world multi-label classification datasets with propositional logic requirements, including a challenging dataset for autonomous driving. Our experimental analysis confirms that CCN+ is able to outperform both its neural counterparts and the state-of-the-art models.

3D mineral prospectivity modeling in the Sanshandao goldfield, China using the convolutional neural network with attention mechanism

Mineralization distribution is spatially heterogeneous and jointly controlled by multiple ore-controlling factors. Given this ubiquitous feature, we integrated the attention mechanism of the Convolutional Block Attention Module (CBAM), which contains both channel and spatial attention modules, into a convolutional neural network (CNN) to develop a more robust 3D mineral prospectivity modeling (MPM) approach. A network interpretive algorithm (DeepLIFT) was conducted to the CNN deconstruction to evaluate whether the key ore-controlling information is read. The Sanshandao goldfield in China, with significant fault controls on gold mineralization distribution, was selected as a case study to verify the proposed method. In this case, we generated the 5-channel images by projecting five ore-related fault features to feed into CNN. The optimal prospectivity model of CNN + CBAM was subsequently yielded by a gradient-based optimization of stochastic objective functions and an optimized regularization cross-entropy loss function. By decomposing the constructed model and the vanilla CNN model, we observed the CNN + CBAM has lower feature scores of fault distance and larger scores of fault geometry (e.g., dip and undulation) at Xiling, Haiyu, and Xinli than CNN. These segments occur significant fault strike or dip changes that have influenced mineralization spatial continuity, which is well consistent with the expert knowledge of metallogeny in the Sanshandao goldfield. This means that the CBAM has induced the CNN to focus on key ore-controlling features from complex exploration data, which is likely to improve the model’s prediction ability. The prospectivity results of CNN + CBAM and other comparison shallow machine learning methods (e.g., random forest) revealed that the CNN + CBAM model produced more reliable results, as evidenced by (1) being highly consistent with known mineralization, (2) having the highest AUC values and success rate, and (3) accurately predicting deep voxels that have been explored by drill holes. Thus, the proposed CNN + CBAM method, with a remarkable capacity for emphasizing critical ore-associated features, is recommended for application in deep 3D MPM. Six possible mineralization sites in the Sanshandao field were identified based on the CNN + CBAM prospective data, which may aid future gold exploration.

Stance-Based Fake News Identification on Social Media with Hybrid CNN and RNN-LSTM Models

Today, fake news can be readily generated and disseminated via social media platforms. Misinformation and hoaxes propagated via online social media or traditional news media are commonly referred to as fake news. Stance-based fake news is based on the opinions of an audience rather than providing correct facts. This study presents a hybrid model focusing on the CNN model and RNN-LSTM model to identify fake news. A balanced dataset of 216k news items called ‘SherLock-FakeNewsNet’ is explored throughout the study resulting in the proposed hybrid model. The NLTK toolkit is utilized to perform some noise reduction. After that, the Keras tokenizer and pre-processor are used for the tokenization and pre-processing steps. Next, the text data is fitted and represented using pre-trained GloVe word embeddings. CNN model is applied with Conv1D layers and MaxPooling layers to extract higher-level features of the text. Following this, to detect the longer context of the given text and to capture interdependencies among word sequences LSTM units are employed in the RNN model. Dropout layers are carefully chosen to reduce overfitting in the final hybrid model. The proposed hybrid model achieves the highest accuracy rate of 92% by outperforming most of the conventional models today with an average Precision of 91%, Recall of 91%, and F1-Score of 91% using an Adam optimizer and a Binary Cross-Entropy loss function. Based on the five experiments performed in the study, we have presented the results by comparing our proposed hybrid model with five related datasets.

COVID-19 Detection on Chest X-Ray Images Using Modified VGG-19

A global pandemic infecting human respiratory system identified as COVID-19 emerged in 2019 has been a critical issue concerning worldwide, including Indonesia. The massive and relatively easy spread of the virus requires COVID-19 patients to be separated from other patients to avoid hospitals being the major source of the virus spread. The gold standard in COVID-19 testing known as rRt-PCR was established to help detecting the virus. This method, however, is found to be less time-efficient due to the sample examination taking 5-7 hours to conduct. Another COVID-19 testing method called Rapid Antigen Test is claimed to be more practical since the sample examination only takes 5-30 minutes to complete. One disadvantage of this testing method is its sensitivity, which is believed to be relatively low. Further supporting examinations are needed in diagnosing COVID-19 when using the Rapid Antigen Test, one of which is chest imaging. Most hospitals in Indonesia are type C hospitals, which means radiology specialists’ availability is limited and sometimes not available. Therefore, it is necessary to develop an automation system for detecting COVID-19 disease on chest X-ray images using the Convolutional Neural Network method to help doctors interpreting chest X-rays of patients effectively faster in determining whether the patient is potentially infected with COVID-19 or not. The algorithm proposed in this study was modified VGG-19, with ADAM optimization method and categorical cross-entropy loss function. This algorithm resulted in a model accuracy rate of 81%, a precision/recall model of 62%, and a specificity model of 87%. The area under the curve in the Receiver Operatic Characteristic Curve indicates that the accuracy level of the test is below the Excellent curve, suggesting that the model created successfully serves as a screening test tool.

Multi-Label Diagnosis of Arrhythmias Based on a Modified Two-Category Cross-Entropy Loss Function

The 12-lead resting electrocardiogram (ECG) is commonly used in hospitals to assess heart health. The ECG can reflect a variety of cardiac abnormalities, requiring multi-label classification. However, the diagnosis results in previous studies have been imprecise. For example, in some previous studies, some cardiac abnormalities that cannot coexist often appeared in the diagnostic results. In this work, we explore how to realize the effective multi-label diagnosis of ECG signals and prevent the prediction of cardiac arrhythmias that cannot coexist. In this work, a multi-label classification method based on a convolutional neural network (CNN), long short-term memory (LSTM), and an attention mechanism is presented for the multi-label diagnosis of cardiac arrhythmia using resting ECGs. In addition, this work proposes a modified two-category cross-entropy loss function by introducing a regularization term to avoid the existence of arrhythmias that cannot coexist. The effectiveness of the modified cross-entropy loss function is validated using a 12-lead resting ECG database collected by our team. Using traditional and modified cross-entropy loss functions, three deep learning methods are employed to classify six types of ECG signals. Experimental results show the modified cross-entropy loss function greatly reduces the number of non-coexisting label pairs while maintaining prediction accuracy. Deep learning methods are effective in the multi-label diagnosis of ECG signals, and diagnostic efficiency can be improved by using the modified cross-entropy loss function. In addition, the modified cross-entropy loss function helps prevent diagnostic models from outputting two arrhythmias that cannot coexist, further reducing the false positive rate of non-coexisting arrhythmic diseases, thereby demonstrating the potential value of the modified loss function in clinical applications.

EEG-Based Evaluation of Aesthetic Experience Using BiLSTM Network

Evaluation of aesthetic design fulfills a pivotal function in product development, which urges for an efficacious objective method to measure customers’ experience. The stability and effectiveness of electroencephalography (EEG) make it a suitable tool for aesthetic experience measurement. Nevertheless, existing studies have several limitations, especially regarding the stimuli and the algorithm. The potential of an EEG-based deep learning model has not been verified in pinpointing subtle differences in physical product aesthetics. To fill the research gap in this issue, we recorded EEG signals in real-life scenarios when participants were presented with different types of physical smartphones, and asked participants to rate them from four dimensions of aesthetic experience (arousal, valence, likeness, and aesthetic evaluation). Then, the time–frequency data were fed into a spatial feature extraction network and an attention-based bidirectional long short-term memory (BiLSTM) optimized by the cross-entropy loss function. The result showed that at 16s window size, the four outcome models yielded the best joint recognition performance of aesthetic experience with an average accuracy of over 85% (arousal: 88.10%, valence: 87.97%, likeness: 85.99%, and aesthetic evaluation: 87.23%). It provides an objective cross-subject recognition method with multi-faceted evaluation results of aesthetic experience. Additionally, we verified the ability of EEG as a reliable and informative resource in terms of aesthetic experience evaluation, even with subtle differences. More practically, a future direction of incorporating EEG signals into subjective product aesthetics measurement could be given more credit.

MMGan: a multimodal MR brain tumor image segmentation method.

Computer-aided diagnosis has emerged as a rapidly evolving field, garnering increased attention in recent years. At the forefront of this field is the segmentation of lesions in medical images, which is a critical preliminary stage in subsequent treatment procedures. Among the most challenging tasks in medical image analysis is the accurate and automated segmentation of brain tumors in various modalities of brain tumor MRI. In this article, we present a novel end-to-end network architecture called MMGan, which combines the advantages of residual learning and generative adversarial neural networks inspired by classical generative adversarial networks. The segmenter in the MMGan network, which has a U-Net architecture, is constructed using a deep residual network instead of the conventional convolutional neural network. The dataset used for this study is the BRATS dataset from the Brain Tumor Segmentation Challenge at the Medical Image Computing and Computer Assisted Intervention Society. Our proposed method has been extensively tested, and the results indicate that this MMGan framework is more efficient and stable for segmentation tasks. On BRATS 2019, the segmentation algorithm improved accuracy and sensitivity in whole tumor, tumor core, and enhanced tumor segmentation. Particularly noteworthy is the higher dice score of 0.86 achieved by our proposed method in tumor core segmentation, surpassing those of stateof-the-art models. This study improves the accuracy and sensitivity of the tumor segmentation task, which we believe is significant for medical image analysis. And it should be further improved by replacing different loss functions such as cross-entropy loss function and other methods.

An Efficient Iris Recognition Technique using CNN and Vision Transformer

The usage of biometric identification has increased in recent years, with numerous public and commercial organizations incorporating biometric technologies into their infrastructures. One of the technologies is iris recognition which has been used as a biometric recognition compared to other modalities to combat identity abuse due to its ability to eliminate risk of collisions or false matches even when comparing large populations. The use of CNN is proven to provide high accuracy; however, this technology involves the need for a large dataset and higher computational cost. Therefore, this study uses a combined model of Convolutional Neural Network (CNN) and Vision Transformer (ViT) in identifying and verifying an iris image. By using the proposed learning rate, it proves that the novel hybrid model is capable to achieve up to 93.66% accuracy in recognizing iris images. The cross-entropy loss function was implemented to reduce the loss and it was able to predict the class label more correctly. In addition, the model was thoroughly tested on three publicly available iris databases, achieving satisfactory iris recognition results. Furthermore, this model has the potential to be used in other biometrics such as face and retina recognitions.

Convolutional Neural Network Driven Computer Vision Based Facial Emotion Detection and Recognition

Computer vision is a sub branch of artificial intelligence (AI) that enables computers and systems to derive substitutive information from digital images and Video. Artificial intelligence plays a significant role in the area of security and surveillance, image processing and machine learning. In computer vision and image processing object detection algorisms are used to detect objects from certain classes of images or video. There is a scope identification of human face emotion Facial emotion recognition is done using computer vision algorism whether the person’s emotion is Happy, sad, fear, disgust, neutral and so on. Object detection algorism are used in deep learning used to classify the detected the regions. Facial emotion recognition is an emerging research area for improving human and computer interaction. It plays a crucial role in security, social communication commercial enterprise and law enforcement. In this research project CNN is used for training the data and predicting seven emotions such as anger, happy, sad, disgust, fear neutral and surprise. In this paper the experiment will be conduct using convolutional neural network as classifier, since it is multi class classification relu, softmax (activation function), categorical cross entropy(loss function) dropout max pooling conducted. The researcher tried to train the model by 80/20, 70/30, 90/10 train test split. However 70/30 train test split out performs over the other. The performance of the model is measured by using the epoch 10 and dropout 0.3. Totally the model is performed 93.8% in the training accuracy and it 75% for the testing.

SENTIMENT ANALYSIS USING LONG TERM MEMORY (LSTM) BOOK CASE STUDY: UNSOLICITED ADVICE FOR MURDERERS BY VERA WONG'S

Reading books is one of the most effective ways to reduce stress. In today's digital era, access to finding and buying books is getting easier, so reader reviews are important in choosing books that match interests. However, with a large number of reviews, Natural Language Processing (NLP) with the Long Short-Term Memory (LSTM) method is used to help analyze positive and negative sentiments from many book reviews. This sentiment analysis is useful for readers to evaluate the quality of books, as well as for authors and sellers to find out the opinions of readers and improve the quality of their work. In this study, the book review dataset "Vera Wong's Unsolicited Advice for Murderers" from the Goodreads website is used, which is then divided into training data and validation data with a ratio of 75%: 25%. The Long Short-Term Memory (LSTM) method is used to analyze the sentiment of the reviews. The model architecture built consists of Embedding Layer, LSTM Layer with 128 neuron units, 3 Dense Layer with ReLU activation function, 3 Dropout Layer, and Fully Connected Layer with and Sigmoid activation function, Binary Cross Entropy loss function, and RMSprop optimizer. The model training process was conducted with 30 epochs. The evaluation results show that the model achieved an accuracy of 90%, indicating the model performs relatively well in correctly classifying positive sentiments.

DualAttNet: Synergistic fusion of image-level and fine-grained disease attention for multi-label lesion detection in chest X-rays

Chest radiographs are the most commonly performed radiological examinations for lesion detection. Recent advances in deep learning have led to encouraging results in various thoracic disease detection tasks. Particularly, the architecture with feature pyramid network performs the ability to recognise targets with different sizes. However, such networks are difficult to focus on lesion regions in chest X-rays due to their high resemblance in vision. In this paper, we propose a dual attention supervised module for multi-label lesion detection in chest radiographs, named DualAttNet. It efficiently fuses global and local lesion classification information based on an image-level attention block and a fine-grained disease attention algorithm. A binary cross entropy loss function is used to calculate the difference between the attention map and ground truth at image level. The generated gradient flow is leveraged to refine pyramid representations and highlight lesion-related features. We evaluate the proposed model on VinDr-CXR, ChestX-ray8 and COVID-19 datasets. The experimental results show that DualAttNet surpasses baselines by 0.6% to 2.7% mAP and 1.4% to 4.7% AP50 with different detection architectures. The code for our work and more technical details can be found at https://github.com/xq141839/DualAttNet.

Enhancing Label Correlations in multi-label classification through global-local label specific feature learning to Fill Missing labels

In multi-label classification, challenges arise from missing labels due to subjective analysis or label ambiguity. This makes it difficult to accurately capture label correlations and enhance classifier performance. Previous research has primarily focused on obtaining label correlations with sparsity assumptions from incomplete label matrices to fill in these missing labels. However, this approach leads to inaccurate label correlations since it relies on incomplete label matrix information. To overcome this issue and improve accuracy, this paper proposes a new method called LCFM, which effectively learns label correlations using a combination of global and local label-specific features to recover missing labels. Firstly, we rely on the sparsity assumption of Lasso regression coefficients to select label-specific features from the training samples. Secondly, we introduce l2-norm regularization to learn global label correlations through global label-specific features. simultaneously, we utilize similar label-specific features to describe local label correlations. Finally, we minimize the difference between predicted results and true labels using the binary cross-entropy loss function and employ the Accelerated Proximal Gradient (APG) algorithm for model optimization. Experimental results comparing LCFM with five benchmark indicators on nine datasets demonstrate that our proposed method outperforms other advanced methods, showing strong competitiveness.

Advanced Meteorological Hazard Defense Capability Assessment: Addressing Sample Imbalance with Deep Learning Approaches

With the rise in meteorological disasters, improving evaluation strategies for disaster response agencies is critical. This shift from expert scoring to data-driven approaches is challenged by sample imbalance in the data, affecting accurate capability assessments. This study proposes a solution integrating adaptive focal loss into the cross-entropy loss function to address sample distribution imbalances, facilitating nuanced evaluations. A key aspect of this solution is the Encoder-Adaptive-Focal deep learning model coupled with a custom training algorithm, adept at handling the data complexities of meteorological disaster response agencies. The model proficiently extracts and optimizes capability features from time series data, directing the evaluative focus toward more complex samples, thus mitigating sample imbalance issues. Comparative analysis with existing methods like UAE-NaiveBayes, UAE-SVM, and UAE-RandomForest illustrates the superior performance of our model in ability evaluation, positioning it as a robust tool for dynamic capability evaluation. This work aims to enhance disaster management strategies, contributing to mitigating the impacts of meteorological disasters.

Efficient Thorax Disease Classification and Localization Using DCNN and Chest X-ray Images.

Thorax disease is a life-threatening disease caused by bacterial infections that occur in the lungs. It could be deadly if not treated at the right time, so early diagnosis of thoracic diseases is vital. The suggested study can assist radiologists in more swiftly diagnosing thorax disorders and in the rapid airport screening of patients with a thorax disease, such as pneumonia. This paper focuses on automatically detecting and localizing thorax disease using chest X-ray images. It provides accurate detection and localization using DenseNet-121 which is foundation of our proposed framework, called Z-Net. The proposed framework utilizes the weighted cross-entropy loss function (W-CEL) that manages class imbalance issue in the ChestX-ray14 dataset, which helped in achieving the highest performance as compared to the previous models. The 112,120 images contained in the ChestX-ray14 dataset (60,412 images are normal, and the rest contain thorax diseases) were preprocessed and then trained for classification and localization. This work uses computer-aided diagnosis (CAD) system that supports development of highly accurate and precise computer-aided systems. We aim to develop a CAD system using a deep learning approach. Our quantitative results show high AUC scores in comparison with the latest research works. The proposed approach achieved the highest mean AUC score of 85.8%. This is the highest accuracy documented in the literature for any related model.

Research on Arrhythmia Classification and Risk Degree Prediction based on Deep Neural Network and Convolutional Neural Network

In this study, a method of arrhythmia classification and risk prediction based on deep neural network and convolutional neural network (CNN) is proposed for ECG data. Electrocardiogram data record the electrophysiological activity of the heart, including normal heart beats and various arrhythmias. In order to monitor and identify arrhythmia in real time and accurately, this study used CNN model for data analysis. The characteristics of CNN, such as local perception, parameter sharing and multi-level feature extraction, make it perform well in ECG data analysis. The data comes from the ' Certification Cup ' Mathematics China Mathematical Modeling Network Challenge in 2023 and is preprocessed to meet the needs of the model. In the process of establishing and solving the model, the cross-entropy loss function is used to optimize, and the effectiveness and robustness of the model are verified by various evaluation methods. The results show that the model can accurately classify and predict the risk of arrhythmia, providing a powerful diagnostic tool for doctors and a valuable reference for future arrhythmia research.

MLMQ-IR: Multi-label multi-query image retrieval based on the variance of Hamming distance

Image retrieval (IR) methods extract the most relevant images to the query images from an image database. The existing IR methods, which retrieve images with a low degree of similarity, use computationally expensive approaches. In this study, a novel Multi-Label Multi-Query IR (MLMQ-IR) method based on the variance of Hamming distance is presented for the query of multiple images having multiple labels. The MLMQ-IR uses deep learning-based hashing code generation with ResNet50 structure. The variance evaluates the minimum variation of the distances between the query and database images, providing the trade-off images according to the center of Pareto space. Moreover, the MLMQ-IR exploits a new Triple Loss Multi-Label Hashing (TLMH) depending on binary cross-entropy loss and bit-balance loss functions. The MLMQ-IR is compared with recent multi-label and multi-query methods through MIRFLICKR-25 K, MS-COCO and NUS-WIDE datasets in terms of three well-known metrics, and the methods are ranked with succussed sorting. As a result, the MLMQ-IR method has the best avg. mean rank with 1.86. The results manifest that the MLMQ-IR provides the most similar retrieved images to the query images owing to utilizing the variance which is the efficient and fast IR approach.